Here’s the truth: many of the forecasting methods hotels still rely on were built for simpler times. Some were designed decades ago. Others were borrowed from entirely different industries.
They weren’t created for perishable inventory, volatile demand, shrinking booking windows, or the constant push and pull of competitor pricing that revenue managers deal with today.
And that’s the problem. Revenue managers don’t lack tools — they lack clarity. With so many forecasting methods on the market, it’s hard to know what’s actually powering your system, where its blind spots are, and whether it’s fit for the decisions you’re expected to make.
Before we break down the four major hotel forecasting methods used today, there’s one concept that changes everything: hotel demand doesn’t live in a straight line. Booking data exists across two dimensions: stay date and lead time. Most forecasting methods only look at one.
Once you understand that limitation, it becomes clear why so many forecasts feel right in theory but fall apart in practice. Knowing how forecasting methods work — and where they fail — is how revenue managers choose systems they can trust, and stop managing around their forecasts instead of with them.
A closer look at the 4 main forecasting methods
1. Statistical methods
Statistical methods like Holt–Winters, SARIMA/SARIMAX, and Prophet are the OGs of revenue management. They rely on mathematical equations that assume the future will behave a lot like the past. These models are “autoregressive,” meaning they predict future demand using historical observations of the same metric, like rooms sold, ADR, or RevPAR, adjusted for trend and seasonality.
In practice, this means the model looks for patterns like weekends vs. weekdays, holidays, or annual peaks, then projects those patterns forward.
Best use case
When the goal is to forecast a single final-day outcome per stay date, such as total rooms sold or ADR, without needing to understand how demand evolves along the way.
Strengths
- Stable and reliable when demand patterns are consistent
- Transparent and explainable, based on well-understood formulas
- Easy to deploy and widely used across the industry
Weaknesses
- They predict one number per stay date, without understanding how bookings accumulate or how pricing and competitor behavior influence demand
- They treat each stay date in isolation (ie, a strong Saturday tells you nothing about Sunday)
- They struggle when demand shifts suddenly due to new competitors, renovations, events, or market shocks
- Accuracy deteriorates quickly as forecasts extend
2. Tree-based methods
Tree-based methods like XGBoost and Decision Tree models take a different approach. Instead of relying on fixed equations, they learn by repeatedly asking questions about past bookings.
Was it a weekend? → Was there a local event? → Was the rate under $150?
After thousands of examples, the model learns that certain combinations, like weekend + festival + competitive rate, reliably lead to higher occupancy. It then continuously compares predictions to actual outcomes and adjusts its internal structure to improve future forecasts.
Best use case
Hotels where demand is strongly influenced by external factors like events, holidays, promotions, or market visibility, and where short-term accuracy matters most.
Strengths
- Can use booking pace and pickup behavior as direct inputs
- Accept a wide range of signals: day of week, events, promotions, reviews, competitor prices, weather
- Automatically determine which factors matter most, without manual weighting
- Perform very well at short lead times, typically within 30 days
Weaknesses
- Learn demand patterns one stay date at a time, without understanding how adjacent dates influence each other
- Can’t recognize that weekends often fill as a block or that long weekends behave as a unit
- Forecast step by step, resulting in small errors that compound quickly
- Accuracy drops sharply as you move beyond short-term horizons
3. Neural networks
Neural networks don’t follow explicit rules or decision trees. Instead, they learn patterns by processing large volumes of data and identifying relationships on their own.
Two architectures are most common in hotel forecasting:
Long Short-Term Memory (LSTM)
LSTMs include a built-in “memory” that decides what information to keep, forget, or emphasize over time. As bookings evolve, the model learns which moments matter most for predicting final demand.
For example, if competitor rates drop sharply 60 days out, the model remembers that signal. If bookings slow initially but historically rebound later, the LSTM can learn that pattern and adjust its forecast accordingly.
Transformers
Transformers take a different approach. Instead of processing data step by step, they look at all time points simultaneously and decide which ones are most relevant using self-attention. This allows them to capture long-range relationships more efficiently.
Best use case
High-volume, city-center hotels with rich datasets, where demand is shaped by many overlapping factors and sufficient technical resources are available.
Strengths
- Capture complex, nonlinear relationships in demand
- Incorporate signals like search activity, pricing trends, and competitor behavior
- Deliver stable performance once properly trained
Weaknesses
- Difficult to train consistently and often require expert tuning
- Require large volumes of clean data and struggle with sparse or irregular demand
- Even with their sophistication, they can’t leverage correlations between adjacent stay dates
Current forecasting methodologies for time series data generally fall into three broad categories: statistical models that use autoregressive components and capture seasonality, tree-based regression model, and neural network architectures. These methods have been widely adopted in both academia and industry due to their adaptability and performance across a range of forecasting tasks. However, it is important to note that all these models traditionally operate on one-dimensional slices of the hotel time surface, such as fixed lead time or fixed report dates.
4. The Cloudbeds approach: Time surface ensemble
Cloudbeds Revenue Intelligence starts from a different premise: hotel demand doesn’t move in a straight line. It unfolds across stay date and lead time simultaneously.
Instead of forcing hotel data into one-dimensional forecasts, Cloudbeds models demand as a two-dimensional time surface, capturing how occupancy, ADR, RevPAR, published rates, and competitor rates evolve together over time.
Rather than relying on a single model, Cloudbeds uses an ensemble of specialized models optimized for different horizons:
- Short-range (0–30 days)
- Medium-range (30–90 days)
- Long-range (90–365 days)
The system learns which models perform best under which conditions and blends their outputs accordingly.
Best use case
This is the apex of hotel forecasting, where small gains in accuracy compound into meaningful revenue impact. It’s especially powerful for larger hotels and hotel groups with rich historical data, but works for properties of all sizes as they accumulate data over time.
Strengths
- Trained on your hotel’s own demand patterns, not market averages
- Consistently low error rates across all forecast horizons
- Up to 65% lower MAE and 50% lower MAPE compared to traditional methods
- Captures correlations between adjacent stay dates that other approaches overlook
- Turns forecast accuracy into confident pricing decisions and measurable revenue uplift
Weaknesses
- Requires a substantial volume of clean, consistent data to perform at its best
Our approach is based on modelling the full two-dimensional time surface (stay date × report date), which allows for a richer representation of booking dynamics.
Why revenue managers should care
Understanding forecasting methods isn’t about becoming a data scientist. It’s about knowing whether the system you rely on is actually built for the decisions you’re responsible for:
Are you using a system built for today’s demand?
Many tools still rely on outdated statistical methods that only see a thin slice of reality. They may look stable on paper, but they can’t account for how demand forms or shifts in real time.
How much confidence should you place in long-term forecasts?
Some methods shine in the short term but quietly fall apart beyond a few weeks. Knowing where accuracy degrades helps revenue managers judge when to trust or challenge a forecast.
Can your RMS provider explain how the forecast is made?
Does it learn from current booking pace or just historical averages? Can it capture correlations between adjacent stay dates, or does it treat every night in isolation? If those answers are vague, that’s a red flag.
What is forecast inaccuracy actually costing you?
Every missed demand signal has a price. Holding rates too long. Dropping them too late. Leaving revenue behind because the forecast didn’t see what was coming.
A forecast you can trust
With Cloudbeds Revenue Intelligence, revenue managers gain forecasts they can trust across both short and long horizons because the system learns from their own hotel’s data, not industry averages.
That accuracy directly translates into better pricing decisions. When on-the-books data looks soft, teams can confidently hold rate if the demand surface shows momentum building ahead. In simulations, this level of precision delivered an average 8.4% revenue uplift, compared to roughly 3% from traditional models.
8.4%
revenue uplift with Cloudbeds
3%
revenue uplift with other models
More importantly, it changes how teams work. Instead of reacting to yesterday’s numbers, revenue managers can spot demand shifts earlier, adjust pricing and promotions sooner, and stay ahead of the market rather than chasing it.
Advanced forecasting doesn’t replace revenue managers. It elevates them — from reactive operators to strategic leaders who shape demand instead of responding to it.
